



import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    MobileBertForMaskedLM,
	    MobileBertForMultipleChoice,
	    MobileBertForNextSentencePrediction,
	    MobileBertForPreTraining,
	    MobileBertForQuestionAnswering,
	    MobileBertForSequenceClassification,
	    MobileBertForTokenClassification,
	    MobileBertModel,
	)







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=64,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=37,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   )       ->   List[Any]:
						UpperCAmelCase_:		Any							      =     parent
						UpperCAmelCase_:		Optional[int]							      =     batch_size
						UpperCAmelCase_:		Any							      =     seq_length
						UpperCAmelCase_:		Union[str, Any]							      =     is_training
						UpperCAmelCase_:		Dict							      =     use_input_mask
						UpperCAmelCase_:		Union[str, Any]							      =     use_token_type_ids
						UpperCAmelCase_:		Tuple							      =     use_labels
						UpperCAmelCase_:		Union[str, Any]							      =     vocab_size
						UpperCAmelCase_:		Optional[int]							      =     hidden_size
						UpperCAmelCase_:		Optional[Any]							      =     embedding_size
						UpperCAmelCase_:		Any							      =     num_hidden_layers
						UpperCAmelCase_:		int							      =     num_attention_heads
						UpperCAmelCase_:		List[Any]							      =     intermediate_size
						UpperCAmelCase_:		Tuple							      =     hidden_act
						UpperCAmelCase_:		Tuple							      =     hidden_dropout_prob
						UpperCAmelCase_:		Dict							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Tuple							      =     max_position_embeddings
						UpperCAmelCase_:		Optional[Any]							      =     type_vocab_size
						UpperCAmelCase_:		Optional[int]							      =     type_sequence_label_size
						UpperCAmelCase_:		Optional[Any]							      =     initializer_range
						UpperCAmelCase_:		int							      =     num_labels
						UpperCAmelCase_:		Union[str, Any]							      =     num_choices
						UpperCAmelCase_:		Tuple							      =     scope

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		str							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		List[str]							      =     None
						UpperCAmelCase_:		Optional[Any]							      =     None
						UpperCAmelCase_:		List[Any]							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Any							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		int							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		Dict							      =     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def      __snake_case       (self					)       ->   Optional[Any]:
						return MobileBertConfig(
						    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   embedding_size=self.embedding_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   is_decoder=SCREAMING_SNAKE_CASE_,   initializer_range=self.initializer_range,   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		Dict							      =     MobileBertModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)

						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)
						self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Optional[Any]							      =     MobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						UpperCAmelCase_:		Tuple							      =     MobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, 2)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						UpperCAmelCase_:		str							      =     MobileBertForPreTraining(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		int							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   next_sentence_label=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.prediction_logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)
						self.parent.assertEqual(result.seq_relationship_logits.shape,   (self.batch_size, 2)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						UpperCAmelCase_:		int							      =     MobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[int]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   start_positions=SCREAMING_SNAKE_CASE_,   end_positions=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.num_labels
						UpperCAmelCase_:		Optional[int]							      =     MobileBertForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.num_labels
						UpperCAmelCase_:		Union[str, Any]							      =     MobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		List[str]							      =     self.num_choices
						UpperCAmelCase_:		Optional[Any]							      =     MobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     input_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		Any							      =     token_type_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		Tuple							      =     input_mask.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		Optional[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Dict							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Union[str, Any]							      =     config_and_inputs
						UpperCAmelCase_:		Union[str, Any]							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        MobileBertModel,
			        MobileBertForMaskedLM,
			        MobileBertForMultipleChoice,
			        MobileBertForNextSentencePrediction,
			        MobileBertForPreTraining,
			        MobileBertForQuestionAnswering,
			        MobileBertForSequenceClassification,
			        MobileBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	(
			    {
			        '''feature-extraction''': MobileBertModel,
			        '''fill-mask''': MobileBertForMaskedLM,
			        '''question-answering''': MobileBertForQuestionAnswering,
			        '''text-classification''': MobileBertForSequenceClassification,
			        '''token-classification''': MobileBertForTokenClassification,
			        '''zero-shot''': MobileBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			A      		=	True

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=False					)       ->   Optional[int]:
						UpperCAmelCase_:		Union[str, Any]							      =     super()._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   return_labels=SCREAMING_SNAKE_CASE_					)

						if return_labels:
									if model_class in get_values(SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		Union[str, Any]							      =     torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length),   dtype=torch.long,   device=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Union[str, Any]							      =     torch.zeros(
												    self.model_tester.batch_size,   dtype=torch.long,   device=SCREAMING_SNAKE_CASE_					)
						return inputs_dict

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Tuple							      =     MobileBertModelTester(self					)
						UpperCAmelCase_:		str							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   str:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Dict							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE_					)







def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple  ):





			"""simple docstring"""


			return torch.tensor(
			    lowerCAmelCase__		,						dtype=torch.long		,						device=lowerCAmelCase__		,						)


a      :  Any			     =			1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class 				_a				(   unittest.TestCase					):

			@slow
			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		int							      =     MobileBertModel.from_pretrained("""google/mobilebert-uncased"""					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]					)
						with torch.no_grad():
									UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_					)[0]
						UpperCAmelCase_:		int							      =     torch.Size((1, 9, 512)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     torch.tensor(
						    [
						        [
						            [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
						            [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
						            [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
						        ]
						    ],   device=SCREAMING_SNAKE_CASE_,   )

						# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
						# ~1 difference, it's therefore not a good idea to measure using addition.
						# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
						# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
						UpperCAmelCase_:		Optional[int]							      =     torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE					)
						UpperCAmelCase_:		List[str]							      =     torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE					)

						self.assertTrue(lower_bound and upper_bound					)











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






